Efficient use of computing resources through transformation and comparison of trade data to musical piece representation and metrical tree

• US 10,672,075 B1
• Filed: 04/02/2019
• Issued: 06/02/2020
• Est. Priority Date: 12/19/2014
• Status: Active Grant

• Current Assignee: Data Boiler Technologies LLC
• Original Assignee: Data Boiler Technologies LLC
• Inventors: To, Kelvin C.
• Primary Examiner(s): Borlinghaus, Jason
• Assistant Examiner(s): Alladin, Ambreen A.
• Application Number:
  US16/373,625
• Time in Patent Office: 427 Days
• Field of Search: 705 37
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G10H 1/0025 Automatic or semi-automatic music composition, e.g. producing random music, applying rules from music theory or modifying a musical piece automatically producing a series of tones G10H1/26
• CPC Class Codes: G10H 2210/111 Automatic composing, i.e. using predefined musical rules
• CPC Class Codes: G10H 2220/351 Environmental parameters, e.g. temperature, ambient light, atmospheric pressure, humidity, used as input for musical purposes
• CPC Class Codes: G10H 2240/181 Billing, i.e. purchasing of data contents for use with electrophonic musical instruments; Protocols therefor; Management of transmission or connection time therefor

Abstract

A system for processing and monitoring trade data receives and transforms trade activities to a musical piece representation, converts the representation to a metrical tree, and performs analysis that provides more accurate onset detection at accelerated speed with more efficient use of computing resources by placing more emphasis on the information hierarchically contained in the metrical tree than on the tree structure when comparing and matching trade patterns, such as potential market manipulation, market change signal, synthetically created trades, or likelihood that the set of trade activities will result in a market price move of one or more financial assets against plans. The analytical system further weights-in the identified signals and determines scores to reflect likelihood of trade irregularities. When the score does not meet a preconfigured threshold, a corresponding action is executed.

Claims

1. A system for processing and monitoring trade data, the system comprising a computing device having at least one processor, the computing device configured to:

receive data representing a set of trade activities;

transform the data representing the set of trade activities to a musical piece representation;

convert the musical piece representation to a metrical tree, wherein notes of the musical piece representation are recorded in labeled leaf nodes of the metrical tree, and the labeled leaf nodes are connected to unlabeled internal nodes;

set an initial default value of a score representing a probability of trade irregularity;

compare the metrical tree to a plurality of trigger trees stored in a lessons database, each trigger tree of the plurality of trigger trees representing a trade irregularity;

identify at least one trigger tree of the plurality of trigger trees indicating at least a closest match to the metrical tree using a nearest-neighbors algorithm, wherein using the nearest-neighbors algorithm further comprises;

computing distances between trees using match values, wherein the distance from a trigger tree of the plurality of trigger trees to the metrical tree is an average chance of finding a labeled leaf from the trigger tree in leaf nodes connected to unlabeled internal nodes of the metrical tree; and

identifying at least a trigger tree having a minimal distance to the metrical tree;

modify the score to represent an increased probability of trade irregularity as a function of the identification of the at least one trigger tree;

compare the score to a preconfigured threshold; and

execute a corresponding action by a review system as a result of the comparison.

2. The system of claim 1, wherein the transforming comprises:

representing trade activities in the set as notes and trade inactivity as rests in the musical piece representation, wherein each note represents a trade order in the set of trade activities;

placing each note representing a buy order on a treble clef in the musical piece representation;

placing each note representing a sell order on a bass clef in the musical piece representation;

setting a pitch of each note in the musical piece representation according to a price value and a length of time a corresponding trade order will remain open;

setting a tempo of the musical piece representation according to an average speed at which trade orders were filled; and

setting a time signature of the musical piece representation according to a combination of trade instrument type, venue, and market conditions of the set of trade activities.

3. The system of claim 1, wherein the converting comprises:

converting each note in the musical piece representation to a leaf node in the metrical tree by the data processor, comprising;

setting, by the data processor, a level of the leaf node in the metrical tree according to a duration of a corresponding note in the musical piece representation;

ordering, by the data processor, a plurality of nodes in the metrical tree according to a time order of the notes in the musical piece representation;

parsing the metrical tree into plurality of tracks, wherein each track comprises a smallest discrete trading unit of the set of trade activities;

aggregating tracks into dimensions using a partially labelled tree comparison method, wherein each dimension is an organized group of multiple discrete trading units.

4. The system of claim 1, wherein each trigger tree of the plurality of trigger trees includes data representing a pattern selected from a group consisting of potential market manipulation, market change signal, synthetically created trades, and probability that the set of trade activities will result in a market price move of one or more financial assets.

5. The system of claim 1, wherein comparing further comprises:

recursively performing a computation of an average of chances of finding a labelled leaf in a descendant of a non-labelled inner node in the metrical tree until no matching trigger tree is found describing trade activity having a time span matching a time span of the data representing the series of trade activities.

6. The system of claim 1, wherein the computing device is further configured to store the data representing the set of trade activities using time-lock cryptography.

7. A computer implemented method for processing and monitoring electronic trade data, comprising:

receiving data representing a set of trade activities by a data processor;

transforming the data representing the set of trade activities to a musical piece representation by the data processor;

converting the musical piece representation to a metrical tree by the data processor, wherein notes of the musical piece representation are recorded in labeled leaf nodes of the metrical tree, and the labeled leaf nodes are connected to unlabeled internal nodes;

setting an initial default value of a score representing a probability of trade irregularity;

comparing the metrical tree to a plurality of trigger trees stored in a lessons database, each trigger tree of the plurality of trigger trees representing a trade irregularity;

identifying at least one trigger tree of the plurality of trigger trees indicating at least a closest match to the metrical tree using a nearest-neighbors algorithm, wherein using the nearest-neighbors algorithm further comprises;

computing distances between trees using match values, wherein the distance from a trigger tree of the plurality of trigger trees to the metrical tree is an average chance of finding a labeled leaf from the trigger tree in leaf nodes connected to unlabeled internal nodes of the metrical tree; and

identifying at least a trigger tree having a minimal distance to the metrical tree;

modifying the score to represent an increased probability of trade irregularity as a function of the identification of the at least one trigger tree;

comparing the score to a preconfigured threshold; and

executing a corresponding action by a review system as a result of the comparison.

8. The method of claim 7, wherein the transforming comprises:

representing trade activities in the set as notes and trade inactivity as rests in the musical piece representation, wherein each note represents a trade order in the set of trade activities;

placing each note representing a buy order on a treble clef in the musical piece representation;

placing each note representing a sell order on a bass clef in the musical piece representation;

setting a pitch of each note in the musical piece representation according to a price value and length of time a corresponding trade order will remain open;

setting a tempo of the musical piece representation according to an average speed at which trade orders were filled; and

setting a time signature of the musical piece representation according to a combination of trade instrument type, venue, and market conditions of the set of trade activities.

9. The method of claim 7, wherein the converting comprises:

converting each note in the musical piece representation to a leaf node in the metrical tree by the data processor, comprising;

setting, by the data processor, a level of the leaf node in the metrical tree according to a duration of a corresponding note in the musical piece representation;

ordering, by the data processor, a plurality of nodes in the metrical tree according to a time order of the notes in the musical piece representation;

parsing the metrical tree into plurality of tracks, wherein each track comprises a smallest discrete trading unit of the set of trade activities;

aggregate tracks into dimensions using a partially labelled tree comparison method, wherein each dimension is an organized group of multiple discrete trading units.

10. The method of claim 7, wherein each trigger tree of the plurality of trigger trees includes data representing a pattern selected from a group consisting of potential market manipulation, market change signal, synthetically created trades, and probability that the set of trade activities will result in a market price move of one or more financial assets.

11. The method of claim 7, wherein comparing further comprises:

recursively performing a computation of an average of chances of finding a labelled leaf in a descendant of a non-labelled inner node in the metrical tree until no matching trigger tree is found describing trade activity having a time span matching a time span of the data representing the series of trade activities.

12. The method of claim 7, further comprising storing the data representing the set of trade activities using time-lock cryptography.

13. A non-transitory computer readable medium comprising computer readable program code embodied therein, the program instructions executable by at least one processor to cause the at least one processor to perform operations comprising:

receiving data representing a set of trade activities by a data processor;

transforming the data representing the set of trade activities to a musical piece representation by the data processor;

converting the musical piece representation to a metrical tree by the data processor, wherein notes of the musical piece representation are recorded in labeled leaf nodes of the metrical tree, and the labeled leaf nodes are connected to unlabeled internal nodes;

setting an initial default value of a score representing a probability of trade irregularity;

comparing the metrical tree to a plurality of trigger trees stored in a lessons database, each trigger tree of the plurality of trigger trees representing a trade irregularity;

identifying at least one trigger tree of the plurality of trigger trees indicating at least a closest match to the metrical tree using a nearest-neighbors algorithm, wherein using the nearest-neighbors algorithm further comprises;

computing distances between trees using match values, wherein the distance from a trigger tree of the plurality of trigger trees to the metrical tree is an average chance of finding a labeled leaf from the trigger tree in leaf nodes connected to unlabeled internal nodes of the metrical tree; and

identifying at least a trigger tree having a minimal distance to the metrical tree;

modifying the score to represent an increased probability of trade irregularity as a function of the identification of the at least one trigger tree;

comparing the score to a preconfigured threshold; and

executing a corresponding action by a review system as a result of the comparison.

14. The medium of claim 13, wherein the transforming comprises:

representing trade activities in the set as notes and trade inactivity as rests in the musical piece representation, wherein each note represents a trade order in the set of trade activities;

placing each note representing a buy order on a treble clef in the musical piece representation;

placing each note representing a sell order on a bass clef in the musical piece representation;

setting a pitch of each note in the musical piece representation according to a price value and length of time a corresponding trade order will remain open;

setting a tempo of the musical piece representation according to an average speed at which trade orders were filled; and

setting a time signature of the musical piece representation according to a combination of trade instrument type, venue, and market conditions of the set of trade activities.

15. The medium of claim 13, wherein the converting comprises:

converting each note in the musical piece representation to a leaf node in the metrical tree by the data processor, comprising;

setting, by the data processor, a level of the leaf node in the metrical tree according to a duration of a corresponding note in the musical piece representation;

ordering, by the data processor, a plurality of nodes in the metrical tree according to a time order of the notes in the musical piece representation;

parsing the metrical tree into plurality of tracks, wherein each track comprises a smallest discrete trading unit of the set of trade activities;

aggregating tracks into dimensions using a partially labelled tree comparison method, wherein each dimension is an organized group of multiple discrete trading units.

16. The medium of claim 13, wherein each trigger tree of the plurality of trigger trees includes data representing a pattern selected from a group consisting of potential market manipulation, market change signal, synthetically created trades, and probability that the set of trade activities will result in a market price move of one or more financial assets.

17. The medium of claim 13, wherein the comparing further comprises:

recursively performing a computation of an average of chances of finding a labelled leaf in a descendant of a non-labelled inner node in the metrical tree until no matching trigger tree is found describing trade activity having a time span matching a time span of the data representing the series of trade activities.

18. The medium of claim 13, further comprising storing the data representing the set of trade activities using time-lock cryptography.